Multi-dimensional Pattern Discovery of Trajectories Using Contextual Information
Mohammad SharifAli Asghar Alesheikh
Faculty of Geomatics EngineeringK. N. Toosi University of Technology, Tehran, Iran
Introduction
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} Movement: moving objects / processes
} Movement point object: tracking / positioning systems
} Trajectory (x, y, t)
} Movement is taking place in different “contexts”
} Contextualized trajectories (x, y, t, c)
} Movement analysis: GKD | similarity measure | pattern discovery
( time, traffic, weather condition, etc.)
Methodology
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} Similarity measure function: Dynamic Time Warping (DTW)- Speech recognition- Suitable for trajectories with missing information - Addresses parametric data very well- Handles trajectory of different sizes- ....
Implementation
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} Mining Similar PatternsLatitude, Longitude, Time
Altitude, Airplane’s ground speed,Time
- Reference Trajectory (in red)- All trajectories (in light green)- Discovered trajectories (in dark green)
Conclusion
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} new insights into the similarity analysis and pattern discovery of trajectories based on spatial and contextual information
} the DTW distance function is shown sensitive to small alterations in context variations
} movements of point objects are highly affected by both internal and external contexts
} added values of context information is significant in discovering patterns among trajectories
Example
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Relative similarity values of four trajectories: (a) Latitude and longitude (2D); (b) Latitude, longitude, and altitude (3D); (c) Latitude, longitude, altitude, airplane speed, and airplane heading; (d) Wind speed and wind direction; (e) All the previous dimensions together
(a) (b) (c) (d) (e)